This application is related to hiding information in content, such as images, video, audio, etc.
In recent years, digital watermarking has emerged as an increasingly active research area. Information may be hidden in images, videos, and audios in a manner imperceptible to human beings. It provides vast opportunities for covert communications. Consequently, methods to detect covert communication are desired. This task is desired, for example, for law enforcement to deter the distribution of child pornography and for intelligence agencies to intercept communications between terrorists. Steganalysis, in this context, refers to detecting whether given set of content, such as an image, has data hidden in the content. On the other hand, steganalysis can serve as an effective way to judge the security performance of steganographic techniques. In other words, a steganographic method should be imperceptible not only to human vision systems, but also to computer analysis.
Images are a common form of content in which data may be hidden. The diverse nature of natural images and the variation of data embedding approaches make steganalysis difficult. However, a cover medium and an associated stego-version, referring here to the cover medium with data hidden therein, generally differ in some respect since the cover medium is generally modified by data embedding Some data hiding methods may introduce a certain pattern in stego-images. For example, in J. Fridrich, M. Goljan and D. Hogea, “Steganalysis of JPEG Images: Breaking the F5 Algorithm”, 5th Information Hiding Workshop, 2002, pp. 310-323, (hereinafter, Fridrich et al.), Fridrich et al. have discovered that the number of zeros in the block DCT (Discrete Cosine Transform) domain of a stego-image can decrease if the F5 embedding method is applied to the stego-image. This feature may therefore be used to determine whether hidden messages are embedded using F5 embedding. There are other findings involving steganalysis which are directed to particular data hiding methods. See, for example, J. Fridrich, M. Goljan and R. Du, “Detecting LSB Steganography in Color and Gray-Scale Images”, Magazine of IEEE Multimedia Special Issue on Security, October-November 2001, pp. 22-28; R. Chandramouli and N. Memon, “Analysis of LSB Based Image Steganography Techniques”, Proc. of ICIP 2001, Thessaloniki, Greece, Oct. 7-10, 2001. However, the particular data embedding method is often not known before conducting steganalysis. A method designed to blindly (without knowing which data hiding method was employed) detect stego-images is referred to as a general steganalysis method. From this point of view, general steganalysis methods have value for deterring covert communications.
In H. Farid, “Detecting hidden messages using higher-order statistical models,” Proceedings of the IEEE Int'l. Conf. on Image Processing 02, vol. 2, pp. 905-908, (hereinafter, Farid), Farid proposed a general steganalysis method based on image high order-statistics. The statistics are based on decomposition of an image with separable quadrature mirror filters, or wavelet filters. The sub-bands' high order statistics are obtained as features for steganalysis. This method was shown to differentiate stego-images from cover media with a certain success rate. In J. Harmsen, W. Pearlman, “Steganalysis of Additive Noise Modelable Information Hiding”, SPIE Electronic Imaging, Santa Clara, January 2003, pp. 20-24, (hereinafter, Harmsen), a steganalysis method based on the mass center (the first order moment) of a histogram characteristic function is proposed. The second, third, and fourth order moments are also considered for steganalysis.